class_docstr: Encoder class for transaction data in Python lists
  
  Parameters
  ------------
  None
  
  Attributes
  ------------
  columns_: list
    List of unique names in the `X` input list of lists
  
  Examples
  ------------
  For usage examples, please see
  https://rasbt.github.io/mlxtend/user_guide/preprocessing/TransactionEncoder/
docstr: Transforms an encoded NumPy array back into transactions.
  
  Parameters
  ------------
  array : NumPy array [n_transactions, n_unique_items]
      The NumPy one-hot encoded boolean array of the input transactions,
      where the columns represent the unique items found in the input
      array in alphabetic order
  
      For example,
      ```
      array([[True , False, True , True , False, True ],
            [True , False, True , False, False, True ],
            [True , False, True , False, False, False],
            [True , True , False, False, False, False],
            [False, False, True , True , True , True ],
            [False, False, True , False, True , True ],
            [False, False, True , False, True , False],
            [True , True , False, False, False, False]])
      ```
      The corresponding column labels are available as self.columns_,
      e.g., ['Apple', 'Bananas', 'Beer', 'Chicken', 'Milk', 'Rice']
  
  Returns
  ------------
  X : list of lists
      A python list of lists, where the outer list stores the
      n transactions and the inner list stores the items in each
      transaction.
  
    For example,
    ```
    [['Apple', 'Beer', 'Rice', 'Chicken'],
     ['Apple', 'Beer', 'Rice'],
     ['Apple', 'Beer'],
     ['Apple', 'Bananas'],
     ['Milk', 'Beer', 'Rice', 'Chicken'],
     ['Milk', 'Beer', 'Rice'],
     ['Milk', 'Beer'],
     ['Apple', 'Bananas']]
    ```
docstr_tok: ["Transforms","an","encoded","NumPy","array","back","into","transactions",".","Parameters","--","--","--","--","--","--","array",":","NumPy","array","[","n_transactions",",","n_unique_items","]","The","NumPy","one-hot","encoded","boolean","array","of","the","input","transactions",",","where","the","columns","represent","the","unique","items","found","in","the","input","array","in","alphabetic","order","For","example",",","``","`","array","(","[","[","True",",","False",",","True",",","True",",","False",",","True","]",",","[","True",",","False",",","True",",","False",",","False",",","True","]",",","[","True",",","False",",","True",",","False",",","False",",","False","]",",","[","True",",","True",",","False",",","False",",","False",",","False","]",",","[","False",",","False",",","True",",","True",",","True",",","True","]",",","[","False",",","False",",","True",",","False",",","True",",","True","]",",","[","False",",","False",",","True",",","False",",","True",",","False","]",",","[","True",",","True",",","False",",","False",",","False",",","False","]","]",")","``","`","The","corresponding","column","labels","are","available","as","self.columns_",",","e.g.",",","[","'Apple","'",",","'Bananas","'",",","'Beer","'",",","'Chicken","'",",","'Milk","'",",","'Rice","'","]","Returns","--","--","--","--","--","--","X",":","list","of","lists","A","python","list","of","lists",",","where","the","outer","list","stores","the","n","transactions","and","the","inner","list","stores","the","items","in","each","transaction",".","For","example",",","``","`","[","[","'Apple","'",",","'Beer","'",",","'Rice","'",",","'Chicken","'","]",",","[","'Apple","'",",","'Beer","'",",","'Rice","'","]",",","[","'Apple","'",",","'Beer","'","]",",","[","'Apple","'",",","'Bananas","'","]",",","[","'Milk","'",",","'Beer","'",",","'Rice","'",",","'Chicken","'","]",",","[","'Milk","'",",","'Beer","'",",","'Rice","'","]",",","[","'Milk","'",",","'Beer","'","]",",","[","'Apple","'",",","'Bananas","'","]","]","``","`"]
code: def inverse_transform(self, array):
      """Transforms an encoded NumPy array back into transactions.
  
      Parameters
      ------------
      array : NumPy array [n_transactions, n_unique_items]
          The NumPy one-hot encoded boolean array of the input transactions,
          where the columns represent the unique items found in the input
          array in alphabetic order
  
          For example,
          ```
          array([[True , False, True , True , False, True ],
                [True , False, True , False, False, True ],
                [True , False, True , False, False, False],
                [True , True , False, False, False, False],
                [False, False, True , True , True , True ],
                [False, False, True , False, True , True ],
                [False, False, True , False, True , False],
                [True , True , False, False, False, False]])
          ```
          The corresponding column labels are available as self.columns_,
          e.g., ['Apple', 'Bananas', 'Beer', 'Chicken', 'Milk', 'Rice']
  
      Returns
      ------------
      X : list of lists
          A python list of lists, where the outer list stores the
          n transactions and the inner list stores the items in each
          transaction.
  
        For example,
        ```
        [['Apple', 'Beer', 'Rice', 'Chicken'],
         ['Apple', 'Beer', 'Rice'],
         ['Apple', 'Beer'],
         ['Apple', 'Bananas'],
         ['Milk', 'Beer', 'Rice', 'Chicken'],
         ['Milk', 'Beer', 'Rice'],
         ['Milk', 'Beer'],
         ['Apple', 'Bananas']]
        ```
  
      """
      return [
          [self.columns_[idx] for idx, cell in enumerate(row) if cell]
          for row in array
      ]
  
file_path: mlxtend/mlxtend/preprocessing/transactionencoder.py
imports: import numpy
  from scipy.sparse import csr_matrix
  from sklearn.base import BaseEstimator, TransformerMixin
label_desc: Use image node_id 4 for calling the TransactionEncoder obj's underlying member method code with example usage: obj.inverse_transform(array) and returns: unknown